In recent years, pixels of some panels are composed of four sub-pixels. There are red (R), green (G), blue (B), and white (W) sub-pixels. This RGBW color system can improve the optical efficiency of liquid-crystal displays, where the sub-pixels are arranged as shown in FIGS. 1 and 2.
U.S. Pat. No. 5,929,843 proposed an RGB-to-RGBW image-data converting and processing method as shown in FIG. 3 where R, G, and B are inputs of the image color, and R′, G′, B′ and W′ are outputs of the image color, and a minimum-value extractor 11 that chooses the value W′ for white light to emit. The algorithm is as follows:
R′=R
G′=G
B′=B
W′=min(R, G, B)
Because image colors red (R), green (G), and blue (B) can be enhanced by the white sub-pixel at the same time, the image luminance can be enhanced by way of the above algorithm. However, the drawback of the algorithm is that the hue and saturation of the original image cannot be preserved. This is caused by the same increment of image colors red (R), green (G), and blue (B), which results in the possibility of changing the ratio of the original image colors red (R), green (G), and blue (B). The change can be understood by the following equation:R:G: B≠(R′+W′):(G′+W′): (B′+W′)
Consequently, the hue and saturation of the image are changed resulting from the ratio of the image colors red (R), green (G), and blue (B) changed. The schematic diagram for color space is shown in FIG. 4. For the convenience of comparison, all schematic diagrams for color space are expressed as two dimensions (G) and (R). In FIG. 4, point A represents the original image color (RGB) while point A′ represents the resultant image color (R′G′B′) after the processing according to the algorithm. By observing FIG. 4, the path for converting point A to point A′ does not pass through the original point, although the method proposed by U.S. Pat. No. 5,929,843 enhancing the luminance whereas the hue and saturation of the original image cannot be preserved.
For improving the drawback that although the method proposed by U.S. Pat. No. 5,929,843 enhancing the luminance whereas the hue and saturation of the original image cannot be preserved, U.S. Pat. No. 6,724,934 proposed a new RGB-to-RGBW image-data numerical converting and processing method.
The method used by U.S. Pat. No. 6,724,934 is that classifying in advance according to the numerical relation among red (R), green (G), and blue (B) data of the image pixel. If the data are classified in block B1, as shown in FIG. 5, thenW′=min(2×R,2×G,2×B)
R′=2×R−W′
G′=2×G−W′
B′=2×B−W′
In FIG. 5, point A represents the original image color (RGB) while point A′ represents the resultant image color (R′G′B′) after the processing according to the algorithm. Converting from point A to point A′ not only increases luminance double but also preserves hues and saturation of original colors. This is due to R:G:B=(R′+W′): (G′+W′): (B′+W′).
However, if the data are classified in block B2, as shown in FIG. 6, after the numerical relation among red (R), green (G), and blue (B) data of the image pixel is classified, thens=1+{min(R,G,B)/[max(R,G,B)−min(R,G,B)]}
W′=min(s×R, s×G, s×B)
R′=s×R−W′
G′=s×G−W′
B′=s×B−W′
In FIG. 6, point B represents the original image color (RGB) while point B′ represents the resultant image color (R′G′B′) after the processing according to the algorithm. Converting from point B to point B′ not only increases luminance s-times but also preserves hues and saturation of original colors. This is due to R:G:B=(R′+W′):(G′+W′):(B′+W′).
Nevertheless, although the method proposed by U.S. Pat. No. 6,724,934 not only increases luminance but also preserves hues and saturation of original colors, the drawback of this algorithm is that the extents of increasing luminance for image colors (RGB) in block B1 and block B2 are different. The extent of increasing luminance for image color in block B1 is 2 while the extent of increasing luminance for image color in block B2 is s (wherein 2≧s≧1). Especially for those high-luminance and high-saturation images in block B2, of which the extents of increasing luminance are quite different from the extent of increasing luminance for image color in block B1. Because the extents of increasing luminance for those high-luminance and high-saturation images in block B2 approximate to 1 whereas the extent of increasing luminance for image color in block B1 is 2. This results in a too large variation of the simultaneous contrast, and the quality and effect of the image display are degraded. Especially when those images display high-luminance, high-saturation colors, and high-luminance but tend to white color at the same time, the whole image quality is mostly degraded.
Aim to the aforementioned drawbacks, the Samsung Company proposed a paper named ‘Implementation of RGBW Color System in TFT-LCDs’ in the SID2004 conference. The paper depicted an RGB-to-RGBW image-data numerical converting and processing algorithm of Adaptive White Scaling (AWS).
Please refer to FIG. 7, at the same time of inputting the original image color (RGB), a prescribed luminance-enhancement gain w will be sent to the color distortion analyzer 22. The color distortion analyzer 22 will calculate the color-distortion value e for the image before and after the luminance enhancement according to the inputted original image color (RGB) data and the luminance-enhancement gain w. If the calculated color-distortion value e is greater than the critical value, the w controller 23 will lower the luminance-enhancement gain w, and a new luminance-enhancement gain w will be sent to the color distortion analyzer 22 to recount the color-distortion value e. Based on this loop, the process will continue until the color-distortion value e is smaller than the critical value. The luminance-enhancement gain w is sent to the RGBW converter 21 at this time.
Accordingly, different images have different luminance-enhancement gains w so as to control the color-distortion value e before and after the luminance enhancement for different images to be lower than the critical value, and to restrain the phenomenon of too large variation of the simultaneous contrast before and after the luminance enhancement for some images.
However, the algorithm depicted in the paper has drawbacks as follows:                1. It is necessary to calculate the color-distortion value e before and after the luminance enhancement repeatedly so as to obtain the best luminance-enhancement gain w for the input image data (RGB). The method will spend complicated and much investment of hardware and image calculation.        2. For reducing the color-distortion value e before and after the luminance enhancement, and improving the phenomenon of too large variation of the simultaneous contrast before and after the luminance enhancement, the Adaptive White Scaling (AWS) algorithm is achieved by decreasing the luminance-enhancement gains w. In other words, although the quality of image display contrast is remedied, the effect of luminance enhancement needed by the system cannot be retained. Please refer to FIG. 8, which shows the color space that is displayed when the luminance-enhancement gain w is 2 (w=2). For reducing the color-distortion value e before and after the luminance enhancement, the luminance-enhancement gain w is decreased (as shown in FIG. 9). Even when those images display high-luminance and high-saturation colors and high-luminance but tend to white color, for the purpose of restraining the phenomenon of too large variation of the simultaneous contrast after the luminance enhancement for images, the luminance-enhancement gain w is obligated to be decreased to 1 approximately (as shown in FIG. 10). As a result, the effect of enhancing the color luminance of whole image is almost lost, and it is not able to achieve the goals of increasing luminance, preserving hues and saturation of colors, and preserving the image-contrast quality concurrently.        